Lines Matching full:points
19 * For a set of points, this function tries to express
23 * generality, for points in a space of arbitrary dimension, and also over
27 * So let us work with the following set of 5 points given by their
30 Vector3d points[5];
31 points[0] = Vector3d( 3.02, 6.89, -4.32 );
32 points[1] = Vector3d( 2.01, 5.39, -3.79 );
33 points[2] = Vector3d( 2.41, 6.01, -4.01 );
34 points[3] = Vector3d( 2.09, 5.55, -3.86 );
35 points[4] = Vector3d( 2.58, 6.32, -4.10 );
42 * best the five above points. To do that, call this function as follows:
47 &points,
56 * instance how near points[0] is from the plane of equation \f$y=ax+bz+c\f$.
57 * Looking at the coords of points[0], we see that:
61 * are near, so points[0] is very near the plane of equation \f$y=ax+bz+c\f$.
64 * @param numPoints the number of points
65 * @param points the array of pointers to the points on which to perform the linear regression
68 data points. The meaning of its coords is as follows.
86 VectorType **points,
92 const int size = points[0]->size();
95 fitHyperplane(numPoints, points, &h);
131 VectorType **points,
139 int size = points[0]->size();
145 mean += *(points[i]);
152 VectorType diff = (*(points[i]) - mean).conjugate();
